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This paper aims to examine the history of data leaks and investigative journalism, the techniques and technology that enable them and their influence in Australia and abroad. It explores the ethical and professional considerations of investigative journalists, how they approach privacy and information-sharing and how this differs from intelligence practice in government and industry. The paper assesses the strengths and limitations of Collaborative Investigative Reporting based on Information Leaks (CIRIL) as a kind of public-facing intelligence practice.

This study draws on academic literature, source material from investigations by the International Consortium of Investigative Journalists and the Organised Crime and Corruption Reporting Project, and a survey of financial crime compliance professionals conducted in 2022.

The paper identifies three key causal factors that have enabled the rise of CIRIL even as traditional journalism has declined: the digital storage of information; increasing public interest in offshore finance and tax evasion; and “virtual newsrooms” enabled by internet communications. It concludes that the primary strength of CIRIL is its creation of complex global narratives to inform the public about corruption and tax evasion, while its key weakness is that the scale and breadth of the data released makes it difficult to focus on likely criminal activity. Results of a survey of industry and government professionals indicate that CIRIL is generally more effective as public information than as an investigative resource, owing to the volume, age and quality of information released. However, the trends enabling CIRIL are likely to continue, and this means that governments and financial institutions need to become more effective at using leaked information.

Over the past decade, large-scale, data-driven investigative journalism projects such as the Pandora Papers and the Russian Laundromat have had a significant public impact by exposing money laundering, financial crime and corruption. These projects share certain hallmarks: the use of human intelligence, often sourced from anonymous leaks; inventive fusion of this intelligence with data from open sources; and collaboration among a global collective of investigative journalists to build a narrative. These projects prioritise informing the public. They are also an important information source for government and private sector organisations working to investigate and disrupt financial crime.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.

The application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely take a perspective of educational technology application to evaluate the application of chatbots to educational contexts. This study aims to bridge the research gap by taking an educational perspective to review the existing literature on artificial intelligence chatbots.

This study combines bibliometric analysis and citation network analysis: a bibliometric analysis through visualization of keyword, authors, organizations and countries and a citation network analysis based on literature clustering.

Educational applications of chatbots are still rising in post-COVID-19 learning environments. Popular research issues on this topic include technological advancements, students’ perception of chatbots and effectiveness of chatbots in different educational contexts. Originating from similar technological and theoretical foundations, chatbots are primarily applied to language education, educational services (such as information counseling and automated grading), health-care education and medical training. Diversifying application contexts demonstrate specific purposes for using chatbots in education but are confronted with some common challenges. Multi-faceted factors can influence the effectiveness and acceptance of chatbots in education. This study provides an extended framework to facilitate extending artificial intelligence chatbot applications in education.

The authors have to acknowledge that this study is subjected to some limitations. First, the literature search was based on the core collection on Web of Science, which did not include some existing studies. Second, this bibliometric analysis only included studies published in English. Third, due to the limitation in technological expertise, the authors could not comprehensively interpret the implications of some studies reporting technological advancements. However, this study intended to establish its research significance by summarizing and evaluating the effectiveness of artificial intelligence chatbots from an educational perspective.

This study identifies the publication trends of artificial intelligence chatbots in educational contexts. It bridges the research gap caused by previous neglection of treating educational contexts as an interconnected whole which can demonstrate its characteristics. It identifies the major application contexts of artificial intelligence chatbots in education and encouraged further extending of applications. It also proposes an extended framework to consider that covers three critical components of technological integration in education when future researchers and instructors apply artificial intelligence chatbots to new educational contexts.

A better understanding of the characteristics and capabilities of women entrepreneurs can significantly improve their chances of success. Therefore, three studies were conducted for this exploratory paper. We have discovered the characteristics of entrepreneurial intelligence among female entrepreneurs through semi-structured interviews based on conventional content analysis. According to the second study, qualitative meta-synthesis was utilized to identify characteristics of women's entrepreneurial intelligence at the international level. As a third study, we examined the evolutionary relationships of entrepreneurs' intelligence components following the discovery and creation of opportunities.

The present paper was based on three studies. In the first study, 15 female entrepreneurs were interviewed using purposive sampling in the Guilan province of Iran to identify the characteristics of entrepreneurial intelligence at the national level. An inductive content analysis was performed on the data collected through interviews. Using Shannon entropy and qualitative validation, their validity was assessed. In the second study, using a qualitative meta-synthesis, the characteristics of women's entrepreneurial intelligence were identified. Then the results of these two studies were compared with each other. In the third study, according to the results obtained from the first and second studies, the emergence, priority and evolution of entrepreneurial intelligence components in two approaches to discovering and creating entrepreneurial opportunities were determined. For this purpose, interviews were conducted with 12 selected experts using the purposeful sampling method using the fuzzy total interpretive structural modeling (TISM) method.

In the first research, this article identified the components of entrepreneurial intelligence of women entrepreneurs in six categories: entrepreneurial insights, cognitive intelligence, social intelligence, intuitive intelligence, presumptuous intelligence and provocative intelligence. In the second study, the components of entrepreneurial intelligence were compared according to the study at the national level and international literature. Finally, in the third study, the evolution of the components of entrepreneurial intelligence was determined. In the first level, social intelligence, presumptuous intelligence and provocative intelligence are formed first and social intelligence and provocative intelligence have an interactive relationship. In the second level, entrepreneurial insight and cognitive intelligence appear, which, in addition to their interactive relationship, take precedence over the entrepreneur's intuitive intelligence in discovering entrepreneurial opportunities. With the evolution of the components of entrepreneurial intelligence in the opportunity creation approach, it is clear that intuitive intelligence is formed first at the first level and takes precedence. At the second level, there is cognitive intelligence is created. At the third level, motivational intelligence and finally, at the last level, entrepreneurial insight, social intelligence and bold intelligence.

This study has the potential to discover credible and robust approaches for further examining the contextualization of women's entrepreneurial intelligence at both national and international levels, thereby advancing new insights. By conceptualizing various components of entrepreneurial intelligence for the first time and exploring how contextual factors differ across nations and internationally for women's entrepreneurship, this paper challenges the assumption that the characteristics of women's entrepreneurial intelligence are uniform worldwide. It also depicts the evolution of the components of entrepreneurial intelligence.

It is important to implement artificial intelligence (AI) because it can simplify and solve complex problems faster than humans. Because AI learns about people and their behavior from the first purchase, AI marketing can boost marketing efforts by leveraging data to target extremely precise consumer groups. There is a debate about the efficacy of AI marketing due to the constraints and limits imposed by the system's nature. This chapter presents insights from published studies regarding the relationship of AI with marketing and how AI can affect marketing. A real-world example of Netflix's usage of AI in marketing has been demonstrated. Then, consumer attitudes regarding AI were revealed. Then, several ethical considerations concerning AI were highlighted. Finally, the anticipated future of AI marketing was addressed. This chapter demonstrated the significance of firms implementing AI marketing to get a competitive advantage. Although some of the difficulties mentioned in this study need to be resolved, AI marketing has a bright future. There are ethical concerns about bias and privacy that should be addressed further. This chapter will encourage firms to use AI systems in marketing, and it will open the door to concerns that will need to be investigated academically in the future.

This study aims to assess the effectiveness of a scale measuring artificial intelligence capabilities by using the resource-based theory. It seeks to examine the impact of these capabilities on the organizational-level resources of dynamic capabilities and organizational creativity, ultimately influencing the overall performance of government organizations.

The calibration of artificial intelligence capabilities scale was conducted using a combination of qualitative and quantitative analysis tools. A set of 26 initial items was formed in the qualitative study. In the quantitative study, self-reported data obtained from 344 public managers was used for the purposes of refining and validating the scale. Hypothesis testing is carried out to examine the relationship between theoretical constructs for the purpose of nomological testing.

Results provide empirical evidence that the presence of artificial intelligence capabilities positively and significantly impacts dynamic capabilities, organizational creativity and performance. Dynamic capabilities also found to partially mediate artificial intelligence capabilities relationship with organizational creativity and performance, and organizational creativity partially mediates dynamic capabilities – organizational creativity link.

The application of artificial intelligence holds promise for improving decision-making and problem-solving processes, thereby increasing the perceived value of public service. This can be achieved through the implementation of regulatory frameworks that serve as a blueprint for enhancing value and performance.

There are a limited number of studies on artificial intelligence capabilities conducted in the government sector, and these studies often present conflicting and inconclusive findings. Moreover, these studies indicate literature has not adequately explored the significance of organizational-level complementarity resources in facilitating the development of unique capabilities within government organizations. This paper presents a framework that can be used by government organizations to assess their artificial intelligence capabilities-organizational performance relation, drawing on the resource-based theory.

The purpose of this study is to identify the process of virtual influencer stickiness in the age of influencer marketing, which has received little attention in the literature. This is essential because the research creates a theoretical model of follower loyalty/stickiness to virtual influencer techniques from the standpoint of influencer marketing, which has a substantial effect on the evolution of the global marketing world.

In 2022, 302 people who currently follow an Instafamous virtual influencer took part in an Instagram self-administered online survey.

The findings show that both expertise and trustworthiness have a positive and significant influence on parasocial interaction, which in turn has a significant influence on virtual engagement and stickiness.

This research will specifically assist international readers in understanding how to harness and increase the efficiency and efficacy of interactive marketing strategies and methods to engage and retain followers of Instafamous virtual influencer. Moreover, the findings will be beneficial to opinion leaders, brand managers, company investors, entrepreneurs and service designers.

1. The study pioneers a holistic virtual follower stickiness mechanism that comprises the role of source credibility, parasocial interaction, informational influence and virtual follower’s engagement and their interrelationship to each other.

2. This study is based on parasocial interaction theory and source credibility theory to understand the relationship between virtual followers and influencers stickiness process at social media platforms.

3. In addition, the study examined the subsequent effects of sources of credibility components on parasocial interaction; as well as, on virtual follower engagement and stickiness.

4. This study also categorized and examined the moderating effects exerted by the genres of informative influence of virtual influencer.

The study pioneers a holistic virtual follower stickiness mechanism that comprises the role of source credibility, parasocial interaction, informational influence and virtual follower’s engagement and their interrelationship to each other.

This study is based on parasocial interaction theory and source credibility theory to understand the relationship between virtual followers and influencers stickiness process at social media platforms.

In addition, the study examined the subsequent effects of sources of credibility components on parasocial interaction; as well as, on virtual follower engagement and stickiness.

This study also categorized and examined the moderating effects exerted by the genres of informative influence of virtual influencer.

Introduction: The concept of diversity encompasses not only an individual’s self-perceptions but also perceptions of others. It exerts an influence on individuals’ social interactions. The promotion of workforce diversity within an organisation holds considerable importance for several reasons. These include the impact of globalisation, the amalgamation of ideas stemming from diverse ethnic groups, the inclusive progress facilitated by individuals from varying economic and religious backgrounds, and the unique perspectives brought to work by those hailing from different regions of the country.

Purpose: Literature exhibits that the perception of organisational justice is a crucial factor in elucidating the dynamic between subordinates and their supervisors, and its consequential effect on the long-term viability of the enterprise.

Methodology: The research reports responses from 107 Delhi NCR employees. Before the main investigation, a pilot study with 20 employees was conducted for assessing the scale’s reliability and validity.

Findings: The current study has provided evidence indicating that different dimensions of organisational justice have a significant impact on individual work behaviour, specifically task performance behaviour (TP), employee silence (ES), and organisational counterproductive behaviour (CBP). Moreover, this impact is influenced by several factors, including emotional intelligence (EI), social desirability, and the age of employees.

Artificial intelligence is one of the most significant and active fields of study in the last few years. Artificial intelligence-derived robotic technologies known as chatbots are gaining interest from both academic and industry sectors. By analyzing the development and patterns of research on the chatbot phenomena within the tourism field, this study seeks to develop a theoretical framework for the interaction between chatbots and tourism.

The Web of Science (WoS) database’s 33 articles on chatbots related to travel and hospitality were examined between 2019 and 2024 using VOSviewer software for bibliometric and thematic content analysis.

Research on chatbots for tourism and hospitality appears to be in its early stages. The factors influencing tourists' intentions to use chatbots have been thoroughly researched; the attitudes, perceptions and behavioral intentions of destinations, travel agencies and restaurant patrons regarding chatbots were examined, and it was found that the quantitative research approach was dominant. In addition, the majority of the studies are based on a particular theory or model.

This is one of the first attempts to directly comprehend and depict the interconnected structures of studies on the interaction between chatbots and tourism through the use of network analysis. Furthermore, the study’s findings can offer academics a comprehensive viewpoint and a reference manual for more accurate assessment and oversight of the chatbot-tourism interaction. Regarding the lack of research on the topic and the fragmented structure of the studies that exist, it is imperative to provide both a comprehensive overview and a roadmap for future investigations into the usage of chatbots in the travel and hospitality sector.

Technology advancement has changed how banks operate. Modernizing technology has, on the one hand, made it simpler for banks to do their daily business, but it has also increased cyberattacks. The purpose of the study is to to determine the factors that have the most effects on online fraud detection and to evaluate the advantages of AI and human psychology research in preventing online transaction fraud. Artificial intelligence has been used to create new techniques for both detecting and preventing cybercrimes. Fraud has also been facilitated in some organizations via employee participation.

The main objective of the research approach is to guide the researcher at every stage to realize the main objectives of the study. This quantitative study used a survey-based methodology. Because it allows for both unbiased analysis of the relationship between components and prediction, a quantitative approach was adopted. The study of the body of literature, the design of research questions and the development of instruments and procedures for data collection, analysis and modeling are all part of the research process. The study evaluated the data using Matlab and a structured model analysis method. For reliability analysis and descriptive statistics, IBM SPSS Statistics was used. Reliability and validity were assessed using the measurement model, and the postulated relationship was investigated using the structural model.

There is a risk in scaling at a fast pace, 3D secure is used payer authentication has a maximum mean of 3.830 with SD of 0.7587 and 0.7638, and (CE2).

This study focused on investigating the benefits of artificial intelligence and human personality study in online transaction fraud and to determine the factors that affect something most strongly on online fraud detection. Artificial intelligence and human personality in the Indian banking industry have been emphasized by the current research. The study revealed the benefits of artificial intelligence and human personality like awareness, subjective norms, faster and more efficient detection and cost-effectiveness significantly impact (accept) online fraud detection in the Indian banking industry. Also, security measures and better prediction do not significantly impact (reject) online fraud detection in the Indian banking industry.